Driverless artificial intelligence framework for the identification of malignant pleural effusion

Our study aimed to explore the applicability of deep learning and machine learning techniques to distinguish MPE from BPE. We initially used a retrospective cohort with 726 PE patients to train and test the predictive performances of the driverless artificial intelligence (AI), and then stacked with...

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Detalles Bibliográficos
Autores principales: Li, Yuan, Tian, Shan, Huang, Yajun, Dong, Weiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557891/
https://www.ncbi.nlm.nih.gov/pubmed/33045678
http://dx.doi.org/10.1016/j.tranon.2020.100896

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7557891/
https://www.ncbi.nlm.nih.gov/pubmed/33045678
http://dx.doi.org/10.1016/j.tranon.2020.100896

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